matlab部分IPT函数的运用

kpca的算法实现源码，实现了一个数据降维方面的经典算法kpca(kpca the algorithm source code, to achieve a data dimensionality reduction classical algorithm kpca)

说明：  利用IDL编程，使用最小距离法对图像进行监督分类(Using IDL programming, using the minimum distance method to supervise the classification)

对脉搏波的波峰波谷以及重搏波拐点的检测，挺好用的。。(On the pulse wave dicrotic wave peaks and valleys as well as the inflection point detection, very good use. .)

解决散斑图像的重建问题,主要是使用了相位恢复算法(to study the picture problem)

1.从图像中识别几何形状的基本方法；基本原理是在于利用点与线的对偶性，将原始图像空间给定的曲线通过曲线表达形式变为参数空间的一个点，这样就把原始图像中给定的检测问题转化为寻找参数空间的峰值问题。 2.基于Gabor变换的人眼检测MATLAB实现。 3.基于Radon的车牌矫正方法。 4.基于模型函数headata产生不同大小的头模型数据。 5.基于函数wdcbm2()设置图像分层阈值压缩参数，实现图像压缩(The basic method of identifying geometry from the image; the basic principle is to use point and line duality, the original image space curve given expression through the curve into a parameter space of a point, so the peak detection problem is given in the original image is transformed into searching the parameter space.)

SIFT特征是图像的局部特征，其对旋转、尺度缩放、亮度变化保持不变性，对视角变化、仿射变换、噪声也保持一定程度的稳定性，亲身检测可用(SIFT feature is a local feature of image. It keeps invariant to rotation, scaling, brightness change, and also keeps a certain degree of stability to view angle change, affine transformation and noise.)

Digital Image Processing writed by Gonzalez second edition of the Chinese version

该程序基于视频统计车流量。该算法通过识别出交通场景图像中复合车辆特征的像素，进行图像分割，并依据提取出来的特征来匹配前后两幅图像中的车辆，从而计算车流量。(The program is based on the video statistics vehicle traffic. The algorithm calculates the traffic volume by recognizing the pixels of the composite vehicle feature in the traffic scene image, segmenting the image, and matching the vehicles in the two images according to the extracted features.)

基于Harris角点检测的图像拼接matlab实现， 主要内容包括Harris角点检测，NCC,RANSAC以及2幅图像的拼接(A image mosaicing algorithem which based on Harris corner feature detection achieved in matlab. The main contents include the Harris corner detector, the NCC, the RANSAC and two image mosaicing)

sift算法在cv领域的重要性不言而喻，该作者的文章引用率在cv界是number1.本篇博客只是本人把sift算法知识点整理了下，以免忘记。本文比较早的一篇博文opencv源码解析之(3)：特征点检查前言1 中有使用opencv自带的sift做了个简单的实验，而这次主要是利用Rob Hess的sift源码来做实验，其实现在的opencv版本中带的sift算法也是Rob Hess的，只是稍微包装了下。 下面来做下试验,试验sift代码采用Rob Hess的代码，opencv目前版本中的sift源码也是采用Rob Hess的。代码可以在他的主页上下载：http://blogs.oregonstate.edu/hess/code/sift/ 这里我下载的是windows版本的，并采用Qt做了个简单的界面。(sift algorithm is self-evident the importance of the cv field, the author of the article referenced in the cv sector is number1 this blog I just sift algorithm knowledge compiled under, so as not to forget. This article compares the earlier blog post opencv source code analysis (3): check the preface of the feature points 1 the opencv bring their own sift to do a simple experiment, but this time Rob Hess' s sift source to experiment. In fact, with the opencv version sift algorithm is Rob Hess, only slightly packing the next. Below to do the next test, test sift code using the code of Rob Hess, sift source opencv version of Rob Hess. The code can be downloaded on his home page: http://blogs.oregonstate.edu/hess/code/sift/ here I downloaded the windows version, and using Qt to do a simple interface.)